Jetson-SLAM
The frontend of Jetson-SLAM will be released as a separate repository as-well.
Authors: Ashish Kumar
This repository contains Jetson-SLAM with FULL-BA back-end Jetson-SLAM is a GPU-thrusted real-time SLAM library for Monocular, Stereo and RGB-D cameras. It can run very high speeds beyond 500FPS on RTX-2070 and Beyond 90FPS on Jetson-NX @320x240 resolution. Please see the Jetson-SLAM Paper for rigorous results over different resolutions, GPUs and comparison with existing VO/VIO/SLAM pipelines.
Jetson-SLAM can run alongside Deep Neural Networks. It is fully behchmarked with VGG
Video
# 1. Main Results
### Datasets
1. [KITTI-Benchmark](https://github.com/zinuok/VINS-MONO)
2. [EuRoC Benchmark](https://projects.asl.ethz.ch/datasets/doku.php?id=kmavvisualinertialdatasets)
3. [KAIST-VIO Benchmark](https://github.com/url-kaist/kaistviodataset)
#### Results on KITTI Benchmark
#### Results on EuRoC Benchmark
#### Results on KAIST-VIO Benchmark
#### Performance with scaled versions of VGG-16 Co-existing on Jetson-NX
### Build Instructions
**Step-1**
Install the dependencies given below:
1. OpenCV 3
2. Eigen3
3. CUDA
4. Pangolin
**Step-2**
Run build.sh
### Run Instructions
Go to execs and run Jetson-SLAM on following choices:
1. Run stereo_kitti for KITTI-Benchmark
2. Run stereo_euroc for EuRoC Benchmark
3. Run stereo_kaistvio for KAIST-VIO Benchmark
4. Run stereo_live for live images from a Stereo-Rig. Please customize the "stereo_live_config.yaml" file for your stereo rig.
### License
Jetson-SLAM is released under a [GPLv3 license].
### Bibtex citation:
@article{kumar2023high,
title={High-speed stereo visual SLAM for low-powered computing devices},
author={Kumar, Ashish and Park, Jaesik and Behera, Laxmidhar},
journal={IEEE Robotics and Automation Letters},
year={2023},
publisher={IEEE}
}